A CT scanner includes an x-ray tube mounted on a rotatable gantry that rotates around an examination region about a z-axis. A detector array subtends an angular arc opposite the examination region from the x-ray tube. The x-ray tube emits radiation that traverses the examination region. The detector array detects radiation that traverses the examination region and generates projection data indicative thereof. A reconstructor processes the projection data and generates volumetric image data indicative of the examination region. However, the volumetric image data does not reflect the spectral characteristics as the signal output by the detector array is proportional to the energy flux integrated over the energy spectrum.
A CT scanner configured for spectral (energy dependent) CT has included a single broad spectrum x-ray tube and an energy-resolving detector array with energy-resolving detectors (e.g., with photon counting detectors, at least two sets of scintillator-photodiode layers with different spectral sensitivities, etc.) and discrimination electronics, a single x-ray tube configured to switch between at least two different emission voltages (e.g., 80 kVp and 140 kVp) during scanning, and/or two or more x-ray tubes configured to emit radiation having different mean spectra. A reconstructor decomposes the signal from the detector into various energy dependent components and reconstructs the individual components, generating spectral volumetric image data that reflects the spectral characteristics, and/or combines the components to produce non-spectral volumetric image data.
Characterization of tissue vascularity and related pathologies such as angiogenesis, necrosis and hypoxia can improve cancer diagnosis by providing valuable information, which can complement other, more standard, techniques such as metabolic FDG-PET and anatomical CT. For instance, cancerous tissue with increased angiogenesis frequently shows increased heterogeneity and irregularity of the blood vessel mesh within or around lesions. In addition, tumor hypoxia or necrosis in the interior of a tumor may show distinguished lower texture characteristics relative to the tumor boundaries. The literature has shown that histogram-based entropy and uniformity are significant descriptors in the practical assessing of the texture coarseness and irregularity of malignant tissues.
With conventional (non-spectral) texture analysis techniques, with respect to spectral CT volumetric image data, it is possible to analyze texture that either arises from attenuation value distribution (related to material density) or, alternatively, its derived spectral image results such as iodine maps. Unfortunately, with respect to spectral CT volumetric image data, it is not possible with conventional (non-spectral) texture analysis techniques to analyze organ and tissue textures in a way which inherently takes into account the full spectral CT information. In addition, conventional CT texture analysis methods usually generate texture maps with lower spatial resolution relative to the original processed spectral CT volumetric image data.